• The Journal of Bigdata
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• v.6 no.1
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• pp.197-206
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• 2021

GPR data is used for underground exploration. The data gathered are interpreted by experts based on experience as the underground facilities often reflect GPR. In addition, GPR data are different in the noise and characteristics of the data depending on the equipment, environment, etc. This often results in insufficient data with accurate labels. Generally, a large amount of training data have to be obtained to apply CNN models that exhibit high performance in image classification problems. However, due to the characteristics of GPR data, it makes difficult to obtain sufficient data. Finally, this makes neural networks unable to learn based on general supervised learning methods. This paper proposes an image classification method considering data characteristics to ensure that the accuracy of each label is similar. The proposed method is based on semi-supervised learning, and the image is classified using clustering techniques after extracting the feature values of the image from the neural network. This method can be utilized not only when the amount of the labeled data is insufficient, but also when labels that depend on the data are not highly reliable.

• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.4
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• pp.104-110
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• 2016

To get the empirical data of GPR detection and to develop the image prosessing program of GPR detection data, GPR detection were proceed by the underground pipes and cavities buried in the Chamber. In the case of non pavement and asphalt pavement, water filled cavity that was buried in 0.7m depth was able to detection. But in the case of 1.0 m and 1.3 m buring depth, water filled cavity was not able to detection. In the case of non-reinforced and reinforced concrete pavement, it was difficult to detect the cavity caused by signal interference. GPRiPP programs was developed for image processing of the GPR detection data. The major processing algorithm were background removal, stacking and gain function. With proper image processing of gain function and background removal in GPRiPP program, it was showed that similar results can be obtained with conventional image processing program.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.2
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• pp.109-118
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• 2022

GPR (Ground Penetrating Radar), developed as a technology for geotechnical investigations such as sinkhole exploration, was used limitedly as a method to resolve undetectable lines in underground facility exploration. To improve the accuracy of underground facility data, the government made it possible to explore underground facilities using a non-metallic pipeline probe from July 2022. However, GPR has a problem in that the exploration rate is lowered in the soil with high moisture content, such as soft soil, such as clay layer, and there is a lot of variation in long-term accuracy. In this study, as a way to improve the accuracy of exploration considering the characteristics of GPR and the environment of underground facilities, we propose a GPR exploration method for underground facilities using permittivity constant correction and pattern analysis of GPR image data. Through this study, the accuracy of underground facility exploration and high reproducibility were derived as a result of field verification applying GPR frequency band and heterogeneous GPR.

• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.39-46
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• 2019

In recently years, it has become important to detect underground objects of various marterials including metals, such as detecting the location of sink holes and pipe. For this reason, ground penetrating radar(GPR) technology is attracting attention in the field of underground detection. GPR irradiates the radar wave to find the position of the object buried underground and express the reflected wave from the object as image. However, it is not easy to interpret GPR images because the features reflected from various objects underground are similar to each other in GPR images. Therefore, in order to solve this problem, in this paper, to estimate the piping position in the GRP image according to the threshold value using the CNN (Convolutional Neural Network) model based on deep running, which is widely used in the field of image recognition, As a result of the experiment, it is proved that the pipe position is most reliably detected when the threshold value is 7 or 8.

• Journal of the Korean Recycled Construction Resources Institute
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• v.5 no.2
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• pp.177-184
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• 2017

Many ground subsidence accidents have happened in Korea. The accident was caused by the subsidence and leakage of the deteriorated sewage pipe. This study aims to establish the empirical data of the ground penetration radar(GPR) detection for ground subsidence. A test bed was also manufactured for the same purpose. The GPR detection variables are embedment depth and horizontal distance of embedded cast iron pipe and expanded polystyrene(EPS). From the detection results, the EPS embedded by a depth of 1.5m was difficult for detection. The EPS closely embedded to the cast iron pipe within a 0.5m distance had a very strong cast iron pipe signal. Therefore, the detection was impossible. This study developed an image processing program, called the GPR image processing program(GPRiPP), to process the GPR detection results. Its major function is the gain function, which amplifies the wiggle wave signal. Compared to the existing programs, the GPRiPP is capable of showing a similar image processing performance.

• Journal of the Korea Society of Computer and Information
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• v.26 no.2
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• pp.27-37
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• 2021

In this paper, we propose Automatic detection system of underground pipe which automatically detects underground pipe to help experts. Actual location of underground pipe does not match with blueprint due to various factors such as ground changes over time, construction discrepancies, etc. So, various accidents occur during excavation or just by ageing. Locating underground utilities is done through GPR exploration to prevent these accidents but there are shortage of experts, because GPR data is enormous and takes long time to analyze. In this paper, To analyze 3D GPR data automatically, we use 3D image segmentation, one of deep learning technique, and propose proper data generation algorithm. We also propose data augmentation technique and pre-processing module that are adequate to GPR data. In experiment results, we found the possibility for pipe analysis using image segmentation through our system recorded the performance of F1 score 40.4%.

• Geomechanics and Engineering
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• v.37 no.5
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• pp.467-474
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• 2024

Monitoring and managing the condition of underground utilities is crucial for ground stability. This study aims to determine whether images obtained using ground penetrating radar (GPR) accurately reflect the characteristics of buried pipelines through image analysis. The investigation focuses on pipelines made from different materials, namely concrete and steel, with concrete pipes tested under various diameters to assess detectability under differing conditions. A total of 400 images are acquired at locations with pipelines, and for comparison, an additional 100 data points are collected from areas without pipelines. The study employs GPR at frequencies of 200 MHz and 600 MHz, and image analysis is performed using machine learning-based convolutional neural network (CNN) techniques. The analysis results demonstrate high classification reliability based on the training data, especially in distinguishing between pipes of the same material but of different diameters. The findings suggest that the integration of GPR and CNN algorithms can offer satisfactory performance in exploring the ground's interior characteristics.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.507-513
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• 2021

GPR (Ground Penetrating RADAR) is a sensor that inspects the pavement state of roads, sinkholes, and underground pipes. It is widely used in road management. MMS (Mobile Mapping System) creates a detailed and accurate road map of the road surface and its surroundings. If both types of data are built in the same area, it is efficient to construct both ground and underground spatial information at the same time. In addition, since it is possible to grasp the road and important facilities around the road, the location of underground pipelines, etc. without special technology, an intuitive understanding of the site is also possible, which is a useful tool in managing the road or facilities. However, overseas equipment to which this latest technology is applied is expensive and does not fit the domestic situation. LiDAR (Light Detection And Raging) and GNSS/INS (Global Navigation Satellite System / Inertial Navigation System) were synchronized in order to replace overseas developed equipment and to secure original technology to develop domestic equipment in the future, and GPR data was also synchronized to the same GNSS/INS. We developed software that performs georeferencing using the location and attitude information from GNSS/INS at the time of acquiring synchronized GPR data. The experiments were conducted on the road site by dividing the open sky and the non-open sky. The road and surrounding facilities on the ground could be easily checked through the 3D point cloud data acquired through LiDAR. Georeferenced GPR data could also be viewed with a 3D viewer along with point cloud data, and the location of underground facilities could be easily and quickly confirmed through GPR data.

• Geophysics and Geophysical Exploration
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• v.26 no.4
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• pp.171-184
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• 2023

Machine learning (ML)-based cavity detection using a large amount of survey data obtained from vehicle-mounted ground penetrating radar (GPR) has been actively studied to identify underground cavities. However, only simple image processing techniques have been used for preprocessing the ML input, and many conventional seismic and GPR data processing techniques, which have been used for decades, have not been fully exploited. In this study, based on the idea that a cavity can be identified using diffraction, we applied ML-based diffraction separation to GPR data to increase the accuracy of cavity detection using the YOLO v5 model. The original ML-based seismic diffraction separation technique was modified, and the separated diffraction image was used as the input to train the cavity detection model. The performance of the proposed method was verified using public GPR data released by the Seoul Metropolitan Government. Underground cavities and objects were more accurately detected using separated diffraction images. In the future, the proposed method can be useful in various fields in which GPR surveys are used.

• International Journal of Highway Engineering
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• v.19 no.5
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• pp.69-75
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• 2017

PURPOSES : The purpose of this study is to evaluate different types of Ground Penetrating Radar (GPR) testing for characterizing the road cavity detection. The impulse and step-frequency-type GPR tests were conducted on a full-scale testbed with an artificial void installation. After analyzing the response signals of GPR tests for detecting the road cavity, the characteristics of each GPR response was evaluated for a suitable selection of GPR tests. METHODS : Two different types of GPR tests were performed to estimate the limitation and accuracy for detecting the cavities underneath the asphalt pavement. The GPR signal responses were obtained from the testbed with different cavity sizes and depths. The detection limitation was identified by a signal penetration depth at a given cavity for impulse and step-frequency-type GPR testing. The unique signal characteristics was also observed at cavity sections. RESULTS : The impulse-type GPR detected the 500-mm length of cavity at a depth of 1.0 m, and the step-frequency-type GPR detected the cavity up to 1.5 m. This indicates that the detection capacity of the step-frequency type is better than the impulse type. The step-frequency GPR testing also can reflect the howling phenomena that can more accurately determine the cavity. CONCLUSIONS :It is found from this study that the step-frequency GPR testing is more suitable for the road cavity detection of asphalt pavement. The use of step-frequency GPR testing shows a distinct image at the cavity occurrences.